class_docstr: This class is intended to represent a set of inter-subject regions
  It should inherit from some abstract multiple ROI class,
  not implemented yet.
docstr: Return a confidence index over the different rois
  
  Returns
  -------
  confid: array of shape self.k
         the population_prevalence
docstr_tok: ["Return","a","confidence","index","over","the","different","rois","Returns","--","--","--","-","confid",":","array","of","shape","self.k","the","population_prevalence"]
code: def roi_prevalence(self):
      """Return a confidence index over the different rois
  
      Returns
      -------
      confid: array of shape self.k
             the population_prevalence
      """
      prevalence_ = np.zeros(self.k)
      subjects = self.subjects
      for j in range(self.k):
          subjj = subjects[j]
          conf = self.confidence[j]
          for ls in np.unique(subjj):
              lmj = 1 - np.prod(1 - conf[subjj == ls])
              prevalence_[j] += lmj
      return prevalence_
  
file_path: nipy/nipy/labs/spatial_models/structural_bfls.py
imports: import numpy
  from scipy import stats
label_desc: Use image node_id 6 for calling the LandmarkRegions obj's underlying member method code with example usage: obj.roi_prevalence() and returns: prevalence_